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RESEARCH Open Access

Augmented reality as multimedia: the casefor situated vocabulary learningMarc Ericson C. Santos1* , Arno in Wolde Lübke1, Takafumi Taketomi1, Goshiro Yamamoto1,Ma. Mercedes T. Rodrigo2, Christian Sandor1 and Hirokazu Kato1

* Correspondence:[email protected] Media DesignLaboratory, Graduate School ofInformation Science, Nara Instituteof Science and Technology, 8916-5Takayama, Ikoma, Nara 630-012,JapanFull list of author information isavailable at the end of the article

Abstract

Augmented reality (AR) has the potential to create compelling learning experiences.However, there are few research works exploring the design and evaluation of AR foreducational settings. In our research, we treat AR as a type of multimedia that issituated in authentic environments and apply multimedia learning theory as aframework for developing our educational applications. We share our experiences indeveloping a handheld AR system and one specific use case, namely, situatedvocabulary learning. Results of our evaluations show that we are able to create ARapplications with good system usability. More importantly, our preliminaryevaluations show that AR may lead to better retention of words and improvestudent attention and satisfaction.

Keywords: Augmented reality, Multimedia learning, Ubiquitous learning, Vocabularylearning

IntroductionAugmented reality (AR) is the seamless integration of virtual objects and real environ-

ments (Azuma 1997). In other words, in AR, computer-generated information is

placed in the world as if they co-exist with real objects. It is an emerging technology

that is finding applications in education because of its possible benefits to teaching and

learning (Wu et al. 2013). However, AR’s practical uses are relatively not well under-

stood compared to those of virtual reality and other technologies (Joseph and Uther

2009). Moreover, few research works have been conducted to substantiate AR’s benefits

to learning (Ibanez et al. 2014).

Research works argue that AR’s strengths and therefore its applicability to education

are embodied cognition (Yang and Liao 2014; Kaufmann et al. 2000; Kaufmann 2002)

and interactivity (Ibanez et al. 2014; Di Serio et al. 2013). As Specht et al. (2011) ex-

plained, AR affords new ways of intuitively interacting with information. Another more

fundamental advantage of AR that is not explored as much is the manner of displaying

visual information. AR is useful for presenting the explicit relationship of virtual contents

to objects found in the real world. For example, Matsutomo et al. (2012) use AR for dis-

playing virtual magnetic fields on physical magnets. Another example is the system of

Tarng and Ou (2012) for animating the life cycle of a virtual butterfly on a real plant.

Aside from embodied interactions with digital information, researchers have shown some

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evidence that presenting digital information together with the context of a real environ-

ment helps memorization (Fujimoto et al. 2013; Fujimoto et al. 2012). They argue that AR

has the potential to ease cognitive load and that using AR allows users to form memory

retrieval cues based on the real environment.

Dede (2011) explains that AR is useful for supporting ubiquitous learning in authen-

tic environments. Ubiquitous learning usually involves the use of mobile devices, such

as smartphones (Joseph and Uther 2009). Based on the location or other context data

of the user, the system can provide some learning content. The role of AR in ubiquitous

learning is to present the information onto the real environment thereby creating a

stronger connection between the digital content and the real environment. Currently,

handheld devices like smartphones are already equipped with cameras and other sen-

sors, enough processing power, and large screens for delivering AR learning experiences

(Billinghurst and Duenser 2012). For example, Kamarainen et al. (2013) used AR as a

feature of their smartphone-based system to support a field trip in a local pond.

As of the time of this writing, though, there has been little empirical evidence collected

to substantiate or refute AR’s potential as a usable carrier of educational content. In a re-

view conducted in 2013, Santos et al. (2014a) found only seven research articles reporting

evidence of AR’s effectiveness in improving learning outcomes. In this review, the re-

searchers observed that AR’s impact on learning outcomes vary from a small negative effect

to a large positive effect. There are many factors attributed to this variation, such as the

comparison being made and the appropriate matching of the technology to pedagogical

needs. However, even with the current state of AR, researchers already report that AR has

positive effects on motivational factors of attention and confidence (Di Serio et al. 2013).

Given that AR is useful for presenting information relevant to places, AR is a good

match for teaching culture and languages (Liu 2009; Liu and Tsai 2013). In this re-

search, we limit language learning to vocabulary learning as the target of AR. In our ap-

proach, we based the requirements of our system on multimedia learning theory,

previous vocabulary learning systems, and teacher’s feedback on AR. AR is a kind of

multimedia that is situated in an authentic environment (Santos et al. 2014d). As such,

multimedia learning theory (Mayer 2009; Mayer 2005) can be applied for designing and

evaluating AR’s benefits to learning. After implementing the system, we conducted sys-

tem usability evaluations using general usability scales and a usability scale designed for

handheld AR. In our investigation, we reiterated some design guidelines for applying

AR to education and added our own design goals. Finally, we evaluated student learn-

ing outcomes and student motivation with our application.

The goal of this study is threefold: We would like to (1) develop an AR application,

(2) test its usability, and (3) test its effects on learning. To these ends, we demonstrate

our development and evaluation framework for prototyping AR learning experiences.

We apply AR to the task of memorizing vocabulary words and test AR’s effect on both

learning and student motivation. Finally, because there is little literature substantiating

the benefits of AR to learning, we test AR’s effectiveness as a platform for a

memorization task and examine its impact on student motivation.

Augmented reality for learningThe general public is becoming more familiar with AR mainly because of AR browsers

used for conveying a variety of location-based information (Grubert et al. 2011).

Santos et al. Research and Practice in Techology Enhanced Learning (2016) 11:4 of 23

Currently, people use some AR browsers to see virtual labels and symbols integrated

with a live video feed of the real environment. This makes understanding location-

related information, such as names of buildings, distances of restaurants, and arrows

for navigation, easier (Fujimoto et al. 2012). In the case of situated vocabulary learning,

instead of displaying names and directions, we designed a system that displays words

and animations to teach new vocabulary words that are relevant to the objects found

within the environment.

Several AR systems have also been developed for educational settings (Santos et al.

2014a). One important work is Construct3D (Kaufmann et al. 2000; Kaufmann 2002)

which uses AR to teach students mathematics and geometry concepts. AR is suitable

for this purpose because students can interact naturally with three-dimensional shapes

without the use of a mouse and keyboard. While wearing a head-mounted display, stu-

dents move around virtual shapes and perform operations on them. Moreover, the stu-

dents see the same virtual shapes allowing them to work together on the same target.

Although Construct3D take advantage of embodied cognition and collaborative learn-

ing, these applications do not use AR for displaying the relationship of the virtual con-

tents to the real environment. In our work, we exploit such AR features by teaching

vocabulary through the relationship between virtual objects and the real environment.

AR running on handheld devices can be used for displaying content in big environ-

ments. Handheld AR has gained attention in the field of educational technology because

of its advantages when applied in ubiquitous learning (Dede 2011), situated cognition

(Specht et al. 2011), and collaboration (Li et al. 2011). Billinghurst and Duenser (2012)

explain that handheld AR technology is already mature for this application. AR software

can already run on mobile phones equipped with fast processors, big display screens, data

connections, built-in cameras, and other sensors. Billinghurst and Duenser (2012) call for

more interdisciplinary research to ground AR applications in learning theories. For our

experiments, we designed AR applications for learning Filipino and German words by ap-

plying the principles of multimedia learning theory (Mayer 2009) and its related research.

Moreover, we considered some feedback from teachers and school administrators in order

to make a practical AR application.

Vocabulary learning systemsMastering a foreign language relies heavily on building vocabulary necessary for listen-

ing, reading, speaking, and writing (Yang 2012). Several creative approaches have been

developed to support such vocabulary learning, including hypertext annotations in e-

learning (Chen et al. 2013), collaborative multimedia (Joseph et al. 2005), word games

(Lin et al. 2008), virtual environments (Pala et al. 2011), and interactions with robots

(Wu et al. 2008). The instructional designs for these prototypes leverage on three main

strategies, namely, repetition, engagement, and context. Acquiring new words requires

repeated exposure to those words (Webb 2007). This includes both memory rehearsal

(e.g., pronouncing the words several times) and spaced exposures (Dempster 1987),

such as encountering the words on different occasions in reading materials and

conversations.

Several sophisticated systems have been developed in order to support context aware-

ness in learning (Ogata et al. 2008; Chen et al. 2009; Petersen et al. 2009). Context is

important to vocabulary learning because students can use it for forming stronger


associations between the new word and the objects in the real world (Ogata et al.

2011). In vocabulary learning, context can take many forms. Researchers have used per-

sonalized learning systems that tailor-fit the vocabulary content to students’ internal

context, i.e., their current level of competence (Yang 2012). Researchers have also built

vocabulary applications that have capitalized on external, physical contexts, such as

studying in a library or eating in the cafeteria (Scott and Benlamri 2010).

Systems using the environment as context

Situated cognition argues that knowledge cannot be abstracted from the situation from

which it was learned. Learning is always embedded in the activity, context, and culture

from which the knowledge was developed (Brown et al. 1989). Learning vocabulary words

from dictionary definitions and a few sample sentences is inferior to conversations and

meaningful bodies of text. The words that students find useful and words they actually

use have better chances of getting acquired. Systems for situated vocabulary learning take

advantage of situated cognition by selecting words that are associated with the environ-

ment and teaching only the words that are useful. Researchers are taking advantage of

near transfer or applying the knowledge learned in a specific situation to an almost similar

context (Dunleavy and Dede 2014). In situated vocabulary learning, the words are learned

in the context of its use thus facilitating knowledge transfer. Moreover, it encourages the

students by illustrating the relevance of the vocabulary words.

Language is always situated in activities that are bound to an environment with its ac-

companying physical, social, and cultural aspects. In two case studies, Wong and Looi

(2010) asked students to take pictures that illustrate English prepositions and Chinese

idioms. For 9 weeks, students used mobile phones to take pictures in school and at

home. They then annotated the pictures with sentences. These sentences were shared

and revised with classmates thereby making the activity collaborative. In their study

with 40 students, they have gathered 481 photo-sentence pairs, 124 revisions, and 134

comments. Although the students enjoyed the activity, they observed that there is a wide

variability in student participation. Students contributed an average of 12.0 (SD = 25.9)

pictures and each offered the revision of 3.1 (SD = 7.3) sentences.

Researchers explain that ubiquitous, context-aware systems are useful for facilitating

situated cognition (Brown et al. 1989). To provide location-aware systems, researchers

have described wireless positioning techniques and content distribution using the wire-

less local area network (WLAN) within their campus (Hsieh et al. 2007; Al-Mekhlafi

et al. 2009; Epp 2013). Using the campus WLAN, Liu (2009) provided the content for

HELLO, an English language learning system. The system detects the location of the

user using quick response (QR) codes spread around the school. At each location,

students practiced conversations with a virtual learning tutor. In their user testing with

64 students, they report that the students who used the situated language learning

approach scored significantly higher (M = 89.4, SD = 7.5) compared to those who used

printed materials and audio recordings (M = 81.3, SD = 9.6). The observed large effect

(d = 1.0) is attributed to practicing English in real-life situations and encouraging the

creativity of the students in handling conversations.

Instead of using WLAN positioning techniques and QR codes, Edge et al. (2011) took

advantage of the sub-categories of Foursquare1 as the classification of the type of


establishment the user is currently in. They then generated the vocabulary words that

are frequently associated with that establishment. The users study these vocabulary

words via a mobile application called MicroMandarin. For 4 weeks, 23 participants

used their system to learn Chinese vocabulary words in establishments in Shanghai and

Beijing. Of all the participants, 68 % felt that the detection of their location was “ok” to

“great” and 91 % found that the vocabulary content was “ok” to “great.”

Similar to MicroMandarin, Vocabulary Wallpaper (Dearman and Truong 2012) is a

microlearning mobile application that takes advantage of idle times that people spend

waiting in different locations. Dearman and Truong prototyped the Vocabulary Wall-

paper for casual learning of Italian users in three types of establishments within the

vicinity of their university. Using GPS or network positioning, Vocabulary Wallpaper

determines which of the predefined establishment the user is in. The researchers tested

the application with 16 participants using it for four sessions. The results show that the

participants can recall an average of 23.3 (SD = 17.1) words and recognize an average of

39.5 (SD = 19.3) words out of all the 75 words. Interestingly, the participants signifi-

cantly (p < 0.05) gained more situated words (M = 9.27, SD = 6.44; M = 7.33, SD = 5.68)

than words that were designed to appear more frequently (M = 6.73, SD = 6.17).

Aside from presenting information related to the user’s current environment, the tagged

added learning object (TANGO) system uses RFID to tag the objects in the environment

to present vocabulary words relevant to an object. They equipped a PDA with an RFID

reader which scans the environment. A question is presented to the users on the PDA

and they answer by tapping their PDAs to the correct object. They evaluated the usability

of TANGO in two user studies. In the first user study with six students (Ogata and Yano

2004), TANGO has a perceived ease of use of 3.3/5 (SD = 1.0) and a perceived usefulness

of 4.2/5 (SD = 0.4). In the second user study with 16 students (Ogata et al. 2010), TANGO

improved its perceived ease of use at 4.3/5 and perceived usefulness at 4.7/5.

Beaudin et al. (2007) took TANGO to the next level by detecting more user interac-

tions with objects inside a house. Aside from tagging objects with RFID, they used

three more sensors: switches for opening and closing cabinets, water flow detectors for

the plumbing system, and piezo-triggered accelerometers to detect movements of ob-

jects. Overall, they tagged over 100 objects inside the house with 400 Spanish phrases.

The system identifies the users through their mobile phones. When they use a particu-

lar object (e.g., open a door, sit on a sofa), the system plays the relevant English word

and its Spanish translation. If they want to browse previously encountered content, they

can access the phrases through their mobile phones. They asked a couple to use the

system for 10 weeks. On the average, the phrases where presented 57 times per hour.

However, even at this intense interaction, the couple found it acceptable even for ex-

tended use. The male participant recalled 158 of the 274 phrases he encountered and

he correctly guessed 65 out of the 126 phrases that were not presented to him. The fe-

male participant recalled 79 of the 178 phrases presented to her and she cor-

rectly guessed 26 of the 92 phrases that were not presented to her.

Connecting vocabulary and the environment using augmented reality

There are several ideas using AR technology to motivate language learning. For ex-

ample, Li et al. (2014) made a flash card interaction for learning English. Our idea is to


use AR for situated vocabulary learning. The most important feature of situated vo-

cabulary learning is the presentation of useful vocabulary words relevant to the current

environment. Based on the ARCS model (Keller 1987), relevance is one of the four fac-

tors to consider in creating motivating instructional materials. ARCS stands for atten-

tion, relevance, confidence, and satisfaction which are the factors contributing to

motivation in using learning materials. Among Keller’s suggestions is relating new in-

formation to something the student is familiar with. In our case, we relate the vocabu-

lary words with a familiar environment.

Existing applications can already deliver the relevant and useful information. How-

ever, the visualization of information remains on the mobile phone screen without

showing the relationship with the real environment. The users are expected to find the

relationship of the vocabulary to their surroundings. This relationship is not always ob-

vious. Using AR, we improved the presentation method by annotating real objects with

sound, text, images, and animations that are 3D-registered onto the environment. This

kind of visualization is beneficial to situated vocabulary learning because it explicitly

illustrates the relationship of the vocabulary with the objects found in the current

environment.

Multimedia learning applied to augmented realityIn multimedia learning theory, multimedia refers to pictures and words (both written

and spoken). It has three assumptions, namely, dual-channels, limited capacity, and ac-

tive processing. First, humans have two separate channels for perceiving visual and

auditory information. Second, individuals can only attend to a limited amount of infor-

mation at any given time. Lastly, learning only takes place if the learner actively pro-

cesses incoming information by connecting it to prior knowledge. Multimedia learning

identifies five processes (Mayer 2009; Mayer 2005) in learning:

1. Selecting words

2. Selecting images

3. Organizing selected words

4. Organizing selected images

5. Integrating information with prior knowledge

Implications of multimedia learning on augmented reality

Situated vocabulary learning leverages on the prior knowledge of places. Visualizing

the information in context-rich environments using AR can aid students in creating

meaningful associations between the content and the real environment. This pro-

motes having a more elaborated knowledge and having more memory retrieval

cues. Situated multimedia aids in the cognitive process of integrating incoming

information with prior knowledge. This is consistent with the findings of Fujimoto

et al. (2013, 2012).

However, AR is also prone to presenting too much information and too much con-

text from the environment leading to cluttered displays (Peterson et al. 2009; Grasset

et al. 2012). This problem arises from the fact that the environment cannot be con-

trolled by the author of the content, whereas all other types of multimedia (books,


computer-based media, virtual environments, etc.) give authors full control of the con-

tent. For example, they can make an illustration as abstract or as contextualized as they

like by removing or adding some details. In the case of AR, the environment is a given

and authors of AR learning contents must make use of the environment creatively.

Cluttered displays hamper the cognitive processes of selecting and organizing. As

such, in order to benefit from AR visualization, we need to make sure that we design

against visual clutter for our AR application. We can confirm if we are successful or

not with the design by conducting usability evaluations (Gabbard and Swan 2008). To

conduct usability evaluations, we can use a general system usability questionnaire like

the system usability scale, or SUS (Lewis and Sauro 2009). Another useful tool is the

Handheld Augmented Reality Usability Scale, or HARUS2 (Santos et al. 2014c; Santos

et al. 2015a), which has a comprehensibility component which measures the ease of un-

derstanding an AR visualization.

Multimedia learning studies in vocabulary learning

Given that individuals have a limited capacity for attending to information, Lin and Yu

(2012) investigated the cognitive load induced by different types of media presentations

on a mobile phone. In their study with 32 eighth graders, they investigated the use of

four multimedia modes, namely, text, text with audio, text with picture, and text with

audio and picture. They discovered that the multimedia mode does not have a signifi-

cant effect on vocabulary gain and retention. However, the learners rated the combined

text-audio-picture as the mode that induced the least cognitive load.

Lin and Wu (2013) investigated the use of these four multimedia modes in a succeed-

ing study with 423 junior high school students. They did not find any significant differ-

ences in vocabulary recognition nor in any interaction between multimedia modes and

learning style preferences of the students. However, the participants who used text with

audio and picture performed best in listening tests followed by the text with sound

group. This result confirms the intuition that audio annotations contribute to the con-

struction of phonological knowledge of words and the application of this knowledge in

listening to sentences. More importantly, they reported that the learning effects of the

audio were maintained for 2 weeks with minimal attrition. Based on these works, we

implemented features in our AR system that allow users to access text, audio, and pic-

tures during the learning scenario.

In a separate study with 121 senior high school students, Lin and Hsiao (2011) stud-

ied the effects of the use of still images against simple animations in vocabulary learn-

ing. Their results showed that the animation group performed significantly better in

learning Chinese and English vocabulary words compared with the image group. They

recommended the use of animations to illustrate dynamic words and processes. Thus,

to facilitate better understanding of vocabulary in our handheld AR system, we in-

cluded a feature wherein sprite sheet animations can be used. We found this feature to

be a simple solution to illustrate verbs in our learning scenario.

Practical considerations in applying augmented realityAside from providing evidence of some benefits in the learning process, AR must

also adhere to some practical considerations in order to adopt them in actual use.


Cuendet et al. (2013) shares five design principles for adopting AR for classroom

use. The five design principles are integrating AR to other class activities, empow-

ering the teacher, providing the teacher awareness of the state of students, flexibil-

ity to adapt the activities to evolving scenarios, and minimizing functionalities to

what is required at a given time.

Based on a survey with teachers and students in Malaysia, Sumadio and Rambli

(2010) observed that although most of them experienced AR for the first time, they

perceived that the demonstrations presented to them are useful for educational prac-

tice. The prototype they showed was an AR learning experience for physics experimen-

tation on heat absorption. Teachers and students expressed that bringing AR to

educational use would make the learning process more enjoyable. The other perceived

benefits are having a better visualization and being able to simulate an experiment be-

fore the actual one. From this example, the participants suggested that it is better to

improve the realism of the virtual objects and expand the prototype to cover other

experiments that are within the Malaysian physics curriculum.

Based on interviews with teachers in the Philippines (Santos et al. 2013; Santos et al.

2015b), AR is perceived to be useful because it offers learning by experiencing some ac-

tivity that cannot be done now in the classroom. In these works, researchers are devel-

oping an AR X-ray system for looking inside objects and inspecting occluded

structures. Currently, even younger learners use desktop computers, smartphones, and

gaming consoles in their daily life. Although more conventional mediums of instruction

will always remain relevant, the teachers would like to take advantage of various

technological interventions to connect with their students. Currently, the teachers are

interested in using AR to motivate class participation and to hold the attention of stu-

dents. This sentiment echos the “empowerment” design principle of Cuendet et al.

(2013) which states that the teacher should remain the central point of class

interaction.

However, the teachers also expressed their concerns about the use of AR technology.

In order to adopt AR technology for the classroom in the next few years, engineers

should consider the cost of the technology, usability, and time constraints, including

the time to set up and covering the required materials for class. This feedback is related

to the “minimalism” design principle of Cuendet et al. (2013) which dictates that the

functionalities engineers should provide must be limited to what is required. More

functionalities than required would make AR more difficult to use.

Design goalsTo summarize what we discussed so far, we list the following design goals based on

multimedia learning, past works on situated vocabulary learning, and some practical

considerations for future adoption to educational settings:

1. Minimize visual clutter on the display

2. Support cognitive processes of selecting, organizing, and integrating information

3. Allow interactions with the environment and objects in the environment

4. Present multimodal information, namely, texts, images, and sounds

5. Use animations when appropriate

6. Apply cheap and accessible technology


7. Make the contents easy to create

8. Limit the interactions

System design and implementationWe created a handheld AR system that can display any combination of multimedia in-

cluding image, animation, sound, and text on a real environment. We then created two

AR applications for learning Filipino and German words in a real environment. We ac-

complished this by simply filling the handheld AR system with content for the situated

vocabulary learning of Filipino and German words.

Handheld augmented reality system

Figure 1 shows the package diagram of our system and Fig. 2 shows the sample inter-

face enabled by our system. The main part of the system is the Controller, which has

access to learning contents, sensors (cameras), and user inputs. The Controller receives

the marker ID and camera view matrix from the Tracker and uses these information to

specify the behavior of the on-screen display. The Tracker was built using ARToolKit

and the Renderer was built on OpenGL ES 2.04.

We used ARToolKit (Kato and Billinghurst 1999) to measure the camera pose with

respect to the target object. Fiducial markers in the video feed were located using the

ARToolKit, which also outputs the marker’s ID and the matrix representing the current

view of the camera. The image was transformed to the correct view using the matrix,

and then it was rendered accordingly using OpenGL ES 2.04.

The AR system runs entirely on iPad tablets. For our experiments, we used the iPad

2 (dual-core A5, 512-MB DDR2 RAM, 32 GB, 601 g, 9.7 in display, 1024 × 768 at

132 ppi) and the iPad mini (64-bit A7, 512-MB DDR2 RAM, 16 GB, 331 g, 7.9 in dis-

play, 1024 × 768 at 163 ppi). The system works with fiducial markers (Fig. 3) to deter-

mine the target object and the viewing angle of the tablet’s back camera. We used the

back camera set to 640 × 480 pixels at 30 fps to sense the marker and to provide a

video feed. After identifying the marker, the system loads the corresponding audio, text,

and image. Audio and text can be accessed using buttons (LISTEN, TRANSLATE,

Fig. 1 Package diagram of our handheld augmented reality system (Santos et al. 2014b)


DESCRIBE). The images can either be still images or sprite sheet animations (Figs. 1

and 3). The images are transformed depending on the camera view and are inserted in

the video feed to suggest 3D registration, that is, to give an impression that the graphics

co-exist with the real objects.

Situated vocabulary learning content

We used the AR system to construct two situated vocabulary learning systems: one for

30 Filipino words and the other for 10 German words. We based the design of the con-

tent from previous works (Lin and Hsiao 2011; Lin and Yu 2012; Lin and Wu 2013) by

using a combination of text, audio, images, and animations as content. The text data

are the vocabulary, its translation in English, and the description of the scene (only for

the Filipino version). The audio data is the proper pronunciation of the vocabulary as

spoken by a native speaker. The image data are text labels, images, or animations, as

shown in Fig. 3.

Fig. 2 Sample interface for situated vocabulary learning

Fig. 3 Displaying nouns using labels and displaying verbs as animations on real objects


User studiesWe explored the strengths of our AR applications for situated vocabulary learning over

its non-AR counterpart (Fig. 4) in two preliminary experiments. In particular, we are in-

terested in the effects of AR on memorization and student motivation. Through these

experiments, we aim to evaluate the use of AR for viewing vocabulary content that is

situated in the real environment. We compared the AR applications to a non-AR version

which is a tablet application that mimics flash card interaction. Our comparison does not

employ any kind of special instructional design, such as game mechanics and collabor-

ation. As summarized in Table 1, users simply point the tablet PC to objects found in their

environment when using our AR application. On the other hand, the flash card applica-

tion allows the user to flip through contents by pressing either next or previous.

We considered inherent features of the interaction as part of the treatment. Thus, we

made no attempts to control them. For example, one advantage of an AR learning sys-

tem is that the students see the real objects in their surroundings even when they are

not studying. We imagine this feature to trigger unintended rehearsal of the vocabulary,

thereby improving memorization. This unintended rehearsal is part of AR learning,

thus, we did not control this aspect. We did not forbid the students in the AR treat-

ment from visiting the study place when they were not studying.

Another inherent feature is that students tend to cover all the vocabulary words sev-

eral times in one study session when flash cards are used. The flash cards are sequen-

tially arranged and students try to go through all the content two to four times in one

sitting. Even if this is the case, interventions were not made because it is an inherent

feature of the use of flash cards. Moreover, advising the students who use the AR appli-

cation to view all the content several times will interrupt their natural learning style.

For our experiments, we controlled both location and time constraints. All of our

students were only allowed to use the applications inside their respective laboratories.

However, the applications are available to them at any time they want to study on that

day. Given these features, we had seven hypotheses which we tested for significance in

Fig. 4 Non-AR version of the AR applications


the 0.05 level via Student’s t test and analysis of covariance (ANCOVA). The hypoth-

eses are as follows:

1. Students will perform worse on a delayed post-test with non-AR compared with the

immediate post-test.

2. Students will perform worse on a delayed post-test with AR compared with the im-

mediate post-test.

3. Students will perform better in an immediate post-test with non-AR.

4. Students will perform better in a delayed post-test with AR.

5. Students will rate AR as a more motivating instructional material.

6. Students will maintain their attention better with AR.

7. Students will find the contents presented through AR to be more relevant to them.

8. Students will feel more confident with non-AR.

9. Students will feel more satisfied with AR.

User testing 1: learning 30 Filipino words in 5 days

We adapted a between-groups approach with 31 participants (26 male, 5 female, aged

23–42, information science graduate students) to test our application for studying

Filipino words. The first languages of the participants are Japanese (13), Chinese (5),

Portuguese (3), German, English, Turkish, Bosnian, Indonesian, Finnish, Arabic, Span-

ish, Nepali, and Wolof. In our experiments, we divided the people into the treatment

groups with consideration to the distribution balance of their first languages.

Eighteen participants were recruited from one laboratory. We set up our system in-

side their laboratory (Fig. 5) so that they can learn words related to their refreshment

Fig. 5 Refreshment area with markers (left), learners using situated vocabulary learning (middle), learnersusing non-AR vocabulary learning (right) (Santos et al. 2014b)

Table 1 Summary of comparison of two interfaces for vocabulary learning

AR application Non-AR application

Interaction Users find an object with a marker. They then pointthe tablet PC to the marker to reveal the content.

Users press “next” or “previous” to switchbetween contents.

Inherentfeature

Users can see the markers in their environment evenwhen they are not studying.

Users can quickly go through all thematerial because they are arrangedin a series.

Visualdisplay

Texts, images, sounds, and animations are displayedin the real environment.

Illustrations are shown on a whitebackground.

Place andtime

Users can only use it inside their laboratory at anygiven time.

Users can only use it inside their laboratoryat any given time.


area. All of them have experienced using an AR application before, thus, AR is not a

novel technology for them. Thirteen participants from three laboratories were asked to

use the non-AR version. Similar to the AR group, the non-AR group had used AR be-

fore and they are familiar with other novel interfaces. We distributed tablet computers

to them with the flash card application installed.

The participants used the assigned application for a recommended duration of 10–

15 minutes per day for 5 days. The AR version was used inside a refreshment area with a

maximum of four people using the application at the same time (Fig. 5). On the other

hand, the learners used the non-AR version whenever they are in their laboratory office.

For our comparative analysis, we evaluated the participants’ learning outcomes and the us-

ability of the application. On the fifth day, the participants answered the SUS to measure the

perceived usability of the applications. They then immediately took a post-test. After 12–

14 days, they took a delayed post-test. The immediate post-test (27 items) and delayed post-

test (24 items) consist of questions on recognizing the word in a multiple choice question,

recalling the translation of the word, and guessing which word fits in different contexts.

For further analysis of the usability of our AR application, we asked the participants in

the AR group to answer the HARUS which measures general system usability, ease of

handling the AR application, and ease of understanding the portrayed information. Lastly,

both AR and non-AR applications logged time-stamped button pushes, words studied,

and tablet acceleration and orientation based on the built-in sensors. We did not notice

any burden on the application due to the system logging even after extended use.

User testing 2: learning 10 German words

We adapted a within-subjects design with 14 participants (8 male, 6 female, aged

17–20, Filipino undergraduate students) to test the application for learning 20

German words (10 for AR and 10 for non-AR). Each participant used the AR and

non-AR versions for a maximum of 8 minutes. Seven used the AR version first,

whereas the other seven used the non-AR version first to balance any effect of the

ordering of the treatment. For the AR version, the learners viewed the content on

a small area around a laboratory technician’s desk. The markers were placed near

each other in a small area to minimize the time spent on transferring from one object to

another. This was important because we wanted to observe the study time of the students.

For the non-AR version, they used the application while sitting inside the same room.

The students are then asked to answer 10 multiple choice questions that test their skill

to recognize a word using a recognition game (Fig. 6). Aside from logging the answer, we

also logged the time it took for the learner to answer the question. After taking the quiz,

the participants also answered a subset of the Instructional Materials Motivational Survey,

or IMMS. We picked 30 questions that are applicable to our system out of the 36 ques-

tions listed in the work of Huang et al. (2006). IMMS models the extent of motivation

one gets from an instructional material by using the ARCS model. This model had been

previously applied to AR instructional materials by Di Serio et al. (2013).

Results and discussionOur experiments involved a small sample size, thus, the results should be interpreted

with caution. These experiments should be replicated with a bigger sample size.


Nevertheless, these results can guide future design of AR applications and experiments

in situated vocabulary learning with AR. In our experiments, we observed significant

decrease in immediate to delayed post-test scores with non-AR but not for AR, sug-

gesting that students who learned via AR retained more vocabulary. No significant dif-

ferences were observed in learning outcomes between using AR and non-AR

applications for vocabulary learning. However, students report better attention and sat-

isfaction in using our system. In summary, we found evidence that supports hypotheses

1, 3, 6, and 9 but not 2, 4, 5, 7, and 8.

Marginally significant differences in usability and learnability

We computed the SUS score and its factors from the participant responses in experi-

ment 1. The AR application scored 74 on the SUS, whereas the non-AR application

scored 80, as shown in Table 2. According to Sauro’s classification (2011), both inter-

faces were above average (SUS score >68). Thus, both interfaces are good. Moreover,

the results in Table 3 show that our participants did not have difficulty in learning how

to operate these new interfaces.

We found a marginally significant difference between the two interfaces with a mod-

erate effect size (d = 0.63). Despite the differences in usability, using these applications

for comparison is reasonable because both represented our best effort and had above

average usability. We achieved a good usability score because we applied insights from

previous research in multimedia learning. Furthermore, our current interface features

were minimal, and the task was simple.

Table 2 Summary of system usability scale scores

Application Number Mean SD T value p value

SUS score AR 18 74 12 1.64 0.055

Non-AR 13 80 6

Fig. 6 Screen capture of the recognition game


More pronounced decrease in post-test scores for non-AR

Table 4 is a summary of the results comparing the immediate and delayed post-test

scores in experiment 1. For the AR group, six people were not able to take the delayed

post-test because they were inaccessible. (They were out-of-town at the time and did

not check their emails 12 to 14 days after the study phase.) Both AR and non-AR

groups decreased from immediate to delayed post-test scores. The difference for the

non-AR group is significant with a large effect (d = 0.84), whereas the differences for

AR is marginally significant, with a small effect size (d = 0.14). Thus, we found evidence

supporting hypothesis 1 but not hypothesis 2.

These results are consistent with the work of Fujimoto et al. (2013, 2012) which re-

ports that information associated with a place is better remembered. In our case, vo-

cabulary that is associated with a place is better remembered than those that were

abstracted (non-AR). However, we believe that an experiment with high sample sizes is

necessary in order to better support this claim and to better understand how familiar

places contribute to the integration process of multimedia learning.

Significantly higher scores with non-AR for immediate post-test but not for the delayed

post-test

Table 5 compares the immediate and delayed post-tests in experiment 1 for AR and

non-AR. In the immediate post-test, the non-AR group scored significantly higher with

a moderate effect (d = 0.75) compared with the AR group, thus supporting hypothesis

3. The breakdown in Table 6 shows that the AR group scored lower than the non-AR

group in all types of questions. This result is indicative of an overall inferior mastery of

content rather than a weakness in a particular question type.

In most practical cases, people do not usually apply their learning immediately after

studying. Rather, they would use their knowledge after a few days, either for a test or to

apply it to a new lesson. As such, the delayed post-test is a more important point of

comparison for learning than the immediate post-test. After 12–14 days, the significant

difference in learning disappeared (Table 7). This is consistent with the results of Lin

and Yu (2012) who reported that various multimedia modes did not have significant

differences. However, the students did report differences in cognitive load. In

Table 3 Summary of SUS factor scores

Factor Application Number Mean SD T value p value

Usability AR 18 70 14 1.50 0.073

Non-AR 13 76 7

Learnability AR 18 90 13 1.53 0.068

Non-AR 13 96 5

Table 4 Comparing immediate and delayed post-tests

Application Post-test Number Mean (%) SD (%) T value p value

AR Immediate 18 71 20 1.46 0.058

Delayed 12 68 23

Non-AR Immediate 13 86 20 3.42 0.001

Delayed 13 70 18


experiment 1, the participants are graduate students who may not be sensitive to differ-

ences in cognitive load induced by an interface. For experiment 2, we asked a younger

group of students to test our interface because they may be more affected by differ-

ences in cognitive load induced by interfaces.

No significant differences in immediate post-test scores after considering usability as co-

variant in ANCOVA

Assuming that implementation quality was a factor affecting the learning of the stu-

dents, we could do fairer comparisons of post-test scores if both AR and non-AR appli-

cations have almost the same SUS score. However, we observed a difference of six SUS

points between the AR and non-AR applications. We conducted ANCOVA to take into

account this difference in usability.

We can conduct ANCOVA because the difference in SUS score was not significant. We

also checked the homogeneity of variances using Levene’s test. The results of Levene’s test

showed that there are no significant differences (p > 0.05) in variances. The ANCOVA re-

sults in Table 8 are almost similar to the ANOVA results in Table 5. Marginally significant

differences were observed in the test scores of AR and non-AR groups for the immediate

post-tests. However, there is almost no difference in the delayed post-tests.

Differences in usage of AR and non-AR applications

To gain further insight regarding the differences between studying with AR and non-

AR applications, we calculated the total amount of time the application is open and the

total number of button pushes for LISTEN, TRANSLATE, and DESCRIBE buttons. We

found that the non-AR application was used significantly longer compared to the AR

Table 5 Comparing scores with AR and non-AR

Post-test Application Number Mean (%) SD (%) T value p value

Immediate AR 18 71 20 2.14 0.020

Non-AR 13 86 20

Delayed AR 12 68 23 0.31 0.380

Non-AR 13 70 18

Table 6 Immediate post-test scores for each question type

Question type Application Number Mean (%) SD (%) T value p value

With illustrations AR 18 87 12 0.99 0.163

Non-AR 13 92 20

Recognizing Filipino with choices AR 18 80 15 2.54 0.008

Non-AR 13 94 15

Recognizing Filipino without choices AR 18 64 30 1.95 0.031

Non-AR 13 83 24

Translating from English to Filipino AR 18 55 31 2.54 0.008

Non-AR 13 81 23

Transfer word usage with choices AR 18 75 19 2.40 0.012

Non-AR 13 91 16


application (Table 9), a finding we already expected after observing the participants’

study on the first day and on the fifth day.

In order to study with the non-AR application, the students had to keep the applica-

tion open for the entire study period. However, when studying with AR, the students

could put the application down and rehearse the words by going through each object

in the room and calling out the vocabulary. In this case, using the application becomes

unnecessary because the room itself represents the learning material. We think this

connection with digital content and the real environment is one important feature of

AR that could be exploited in ubiquitous learning.

We also found some differences in the amount of buttons pushed in the AR applica-

tion compared with the non-AR counterpart. All the three buttons (LISTEN, TRANS-

LATE, DESCRIBE) where used more in general, with the TRANSLATE button being

pushed significantly more. This could mean that AR may be more motivating for stu-

dents, especially for maintaining attention as Di Serio et al. reported (2013). In another

study, Ibanez et al. (2014) reported AR’s influence on learners’ flow state, specifically on

concentration, distorted sense of time, sense of control, clearer direct feedback, and

autotelic experience. As such, for experiment 2, we applied the IMMS similar to Di

Serio et al. (2013) to observe motivation. For experiment 2, we removed the DESCRIBE

button because students did not use it so much and we did not see any significant dif-

ferences in its use (Table 10).

No significant differences in recognition test, but significantly better attention and

satisfaction with AR

There was no significant difference between the recognition test between using AR

(M = 94 %, SD = 8 %) and using non-AR (M = 95 %, SD = 8 %) for vocabulary learning. On

Table 7 Delayed post-test scores for each question type

Question type Application Number Mean (%) SD (%) T value p value

With illustrations AR 12 71 27 0.26 0.400

Non-AR 13 73 16

Recognizing Filipino with choices AR 12 67 23 0.70 0.247

Non-AR 13 72 13

Recognizing Filipino without choices AR 12 69 30 0.09 0.463

Non-AR 13 71 27

Translating from English to Filipino AR 12 65 28 0.10 0.462

Non-AR 13 64 33

Transfer word usage with choices AR 12 64 25 0.87 0.196

Non-AR 13 71 19

Table 8 Analysis of covariance of post-test scores with system usability scale score as covariant

Post-test Application Number Mean (%) SD (%) Adjusted mean (%) F value p value

Immediate AR 18 71 20 72 3.02 0.09

Non-AR 13 86 20 85

Delayed AR 12 68 23 69 0.00 1.00

Non-AR 13 70 18 69


the average, the non-AR group answered our multiple questions faster (M = 2.28 s,

SD = 0.92 s) than the AR group (M = 2.60 s, SD = 1.03 s) for each question. However, this

difference was not significant.

Experiment 2 focuses on evaluating motivation by using the ARCS model. Although

two interfaces can arrive at the same learning outcome, performance in tests should

not be the only measure of success in creating interfaces. User experience is another

important consideration. As such, we also evaluated the interfaces in terms of its ability

to motivate students to learn.

Overall, we only observed a marginally significant difference between the IMMS rat-

ing of AR and non-AR vocabulary learning (Table 11). However, looking at the factors

of the IMMS (Table 12), significant differences were observed in the attention and sat-

isfaction factors. The students report that the AR application catches and holds their

attention more than the non-AR application. This is consistent with the observations

of Di Serio et al. (2013). Moreover, they report higher satisfaction with their learning

experience. The learners were slightly more confident to use non-AR probably because

it is a more familiar interface. This finding is opposite of that of Di Serio et al. (2013).

The learners rated AR to be higher in relevance by five points, which is attributed to

the implicit connection between learning contents and real environment. However, no

statistical significance was observed for the relevance and confidence factors.

Usability, manipulability, and comprehensibility of our AR application for situated

vocabulary learning

Aside from the system usability scale, we used HARUS (Santos et al. 2014c; Santos

et al. 2015a) to measure the system usability of our system. HARUS is specifically de-

signed for handheld AR. It has two factors relevant to AR, namely, manipulability and

comprehensibility. Manipulability corresponds to the ease of handling the device when

doing certain tasks. Usability questionnaires for software and mobile phones do not

usually cover manipulability because software tends to be stationary and mobile phones

tend to be held with a fixed posture. AR, on the other hand, requires the user to move

around while pointing their handheld devices at various angles. This can be difficult

sometimes due to unstable tracking of the natural environment, among other reasons.

Table 9 Duration of application use (in minutes)


Usage AR 18 29.7 10.7 2.88 0.004

Non-AR 13 55.8 36.5

Table 10 Total buttons pushed

Button Application Number Mean SD T value p value

Listen AR 18 408 364 1.01 0.160

Non-AR 13 262 168

Translate AR 18 40 23 2.32 0.015

Non-AR 13 16 23

Describe AR 18 69 70 0.35 0.365

Non-AR 13 58 88


The second factor of HARUS is comprehensibility which is the ease of understanding

the presented information. Although comprehensibility is common to all types of soft-

ware, HARUS is designed for users to respond to AR-specific issues, such as the align-

ment of virtual contents and real environments, visual clutter, and depth perception.

Table 13 summarizes the HARUS score and its factors for the AR application. Our

current prototype scored 61 (out of 100) in terms of overall usability, with a score of 63

on manipulability and 59 on comprehensibility. Compared to the usability score of 74,

we think that we got a lower usability score from HARUS because it is more sensitive

to AR applications. This current score can be used as a reference for the next iteration

of our application. It could also be used as a benchmark for other AR applications for

situated vocabulary learning. Through the use of HARUS, we may be able to compare

handheld AR systems more accurately. However, its results should be interpreted with

caution because HARUS is a relatively new questionnaire with some evidence of valid-

ity and reliability.

One of the straightforward ways to improve our system is to use lighter devices. Some

students reported that the iPad 2 is too heavy for our purpose and it requires the use of

two hands. Another way to improve the manipulability of our system is to use some ergo-

nomically designed handle for tablets, such as the work of Veas and Kruijff (2008).

We think that applying markerless tracking, such as point-cloud-based tracking using

the PointCloud SDK,3 would decrease comprehensibility if we cannot detect good

enough features to track the environment. Moreover, such feature registration process

would be difficult to create if the content authors are teachers. For our current applica-

tion, simply printing markers and placing them in the environment is an easier and

more stable way of tracking the environment. However, we expect both markerless

tracking technology and tablet computing power to improve significantly in the next

few years. At that time, switching to markerless tracking would be practical.

ConclusionsAugmented reality is useful for presenting situated multimedia in ubiquitous learning.

In our work, we discussed our experience in developing and evaluating an AR

Table 11 Summary of the Instructional Materials Motivational Survey scores


Motivation score AR 14 76 12 1.34 0.096

Non-AR 14 71 11

Table 12 Factors of the Instructional Materials Motivational Survey score

Factors Application Number Mean SD T value p value

Attention AR 14 75 14 1.84 0.038

Non-AR 14 65 14

Relevance AR 14 74 14 0.97 0.172

Non-AR 14 69 13

Confidence AR 14 80 12 0.74 0.232

Non-AR 14 83 8

Satisfaction AR 14 77 16 1.71 0.049

Non-AR 14 66 18


application for learning experiences based on an authentic environment. As part of our

development process, we drew design goals from multimedia learning theory, past

systems for vocabulary learning, and needs of teachers. We then created a

handheld AR system for displaying situated multimedia (text, image, sound, and

animation). As a use case of the AR system, we filled the system with Filipino and

German vocabulary contents, thereby creating two AR applications for situated

vocabulary learning.

We evaluated the AR applications by combining methods in human-computer inter-

action, usability engineering, and education technology. We observed differences in im-

mediate post-tests wherein students who used the non-AR application performed

better than those who used AR. This effect is only temporary as both AR and non-AR

users have almost equal scores in the delayed post-tests. We observed a larger differ-

ence between immediate post-test and delayed post-test for the students who used the

non-AR application. This suggests that using AR resulted in better retention. This

result needs to be explored further because our evaluation involved a small sample

size only.

Aside from differences in post-tests, the potential of AR lies in the difference in the

learning experience, more specifically, reducing cognitive load, improving attention,

and increasing satisfaction. Although preliminary, our experiments suggest that AR as

multimedia may lead to better attention and satisfaction.

For future work, experiments with bigger sample size must be used to explore deeper

into how students can learn better with AR. Moreover, aside from cross-sectional

studies comparing AR with a more traditional interface, longitudinal studies are

necessary to explore the evolution of students’ knowledge and skills over time.

Endnotes1https://foursquare.com/2http://harusability.com/3http://developer.pointcloud.io/

Competing interestsHK is one of the inventors of the ARToolKit used to develop this project. As such, we are inclined to use ARToolKit.The version we used is freely available at artoolkit.org. MECS is the developer of the HARUS questionnaire, one of thequestionnaires used for the evaluations. As such, we are inclined to use HARUS. The HARUS questionnaire is freelyavailable at harusability.com.

Authors’ contributionsMECS did the preliminary investigation of prior work, proposed the initial designs of the system and experiments,executed the experiments and analysis, and drafted the manuscript. AWL made revisions on the initial design of thesystem, implemented the system, and helped execute the second experiment (learning German). TT, GY, CS, and HKguided the design and implementation of the system by providing software libraries or code snippets and expertevaluation by inspecting the system. They commented on the concept of the experiment leading to improvements,and they cooperated to form the outline of the manuscript before it was drafted. MMTR contributed the relevantframeworks and analysis used in this article, commented on the experimental design and results leading to insights,and helped revise the manuscript. CS contributed the relevant frameworks and analysis for usability evaluation. HKprovided a modified version of the ARToolKit which MECS and AWL used to implement the system. All authors readand approved the final manuscript.

Table 13 Summary of HARUS scores and its factors

HARUS Manipulability Comprehensibility

AR 61 63 59


https://foursquare.com/

http://harusability.com/

http://developer.pointcloud.io/

Authors’ informationMECS is a post-doctoral researcher at Nara Institute of Science and Technology (NAIST). He received his Dr Eng inInformation Science from NAIST in 2015, his MS in Electronics Engineering and BS in Electronics and CommunicationsEngineering from Ateneo de Manila University in 2009 and 2011, respectively. In 2015, he received a grant from theJapan Society for the Promotion of Science to study augmented reality for learning support. His main researchinterests are in augmented reality and usability engineering.AWL is a doctoral student at Nara Institute of Science and Technology (NAIST), Japan. He received his MS inInformation Science from NAIST in 2013 and his BS in Computer Science and Media Applications from OsnabrückUniversity of Applied Sciences in 2011. He is interested in real-time computer graphics and physics-based animation.TT has been an assistant professor at Nara Institute of Science and Technology (NAIST) since 2011. He received his DrEng and ME degrees in Information Science from NAIST in 2011 and 2008, respectively, and his BE from SaseboNational College of Technology in 2004. He was a visiting research professor in the Department of InformationProcessing Science at the University of Oulu in 2013. He is interested in 3D reconstruction, novel view generation,and augmented reality.GY is an assistant professor at Nara Institute of Science and Technology. He received his BE, ME, and PhD degreesfrom Osaka University in 2004, 2006, and 2009, respectively. He was a JSPS Research Fellow from 2008 to 2009. In2009, he joined the Graduate School of Natural Science and Technology at Okayama University. He was a visitingresearch professor in the Department of Information Processing Science at University of Oulu in 2012. His majorinterests are in human-computer interaction, projection-based augmented reality, and wearable computing systems.MMTR is a professor at Ateneo de Manila University and the head of the Ateneo Laboratory for the Learning Sciences.She received her PhD in Computer Technology in Education from Nova Southeastern University in 2002, her MS inApplied Computer Science from University of Maryland Eastern Shore in 1992, and her BS in Computer Science fromAteneo de Manila University in 1988. In 2008, she was a Fulbright Senior Research Fellow at the Pittsburgh Science ofLearning Center. Her areas of interest are affective computing and artificial intelligence in education.CS is an associate professor at Nara Institute of Science and Technology (NAIST). Since 2000, his foremost researchinterest is augmented reality, as he believes that it will have a profound impact on the future of mankind. In 2005, heobtained a doctorate in computer science from Technische Universität München. He has lived outside of Germanyever since to work with leading research groups at institutions including Columbia University, Canon’s Leading-EdgeTechnology Research Headquarters, Graz University of Technology, University of Stuttgart, and Tohoku University.Before joining NAIST, he directed the Magic Vision Lab at University of South Australia. In 2014, he received a GoogleFaculty Award for creating an Augmented Reality X-Ray system for Google Glass.HK is a professor at Nara Institute of Science and Technology (NAIST) since 2007. He received his BE, ME, and Dr Engdegrees from Osaka University, Japan, in 1986, 1988, and 1996, respectively. When he joined the Human InterfaceTechnology Laboratory (HIT Lab) at University of Washington as a visiting scholar in 1998, he started his research onaugmented reality which is currently his main research interest. He received the Virtual Reality Technical AchievementAward from IEEE Visualization and Graphics Technical Committee in 2009, and Lasting Impact Award at the 11th IEEEInternational Symposium on Mixed and Augmented Reality in 2012.

AcknowledgementsThis work was supported by the Grant-in-Aid for JSPS Fellows, Grant Number 15J10186.

Author details1Interactive Media Design Laboratory, Graduate School of Information Science, Nara Institute of Science andTechnology, 8916-5 Takayama, Ikoma, Nara 630-012, Japan. 2Ateneo Laboratory for the Learning Sciences, Ateneo deManila University, Katipunan Ave., Quezon City, Metro Manila 1108, Philippines.

Received: 28 December 2015 Accepted: 4 January 2016

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